SDG’s and ESG · Source: OECD Science, Technology and Innovation Outlook 2016. Eduardo de Rezende...

Big Data Analytics ,SDG’s and ESG

Eduardo de Rezende [email protected]

Advancing the SDGs: Understanding and Improving the Role of Corporate Transparency through Big Data, São Paulo, SP, Brazil, July 16-17, 2018


Advancing the SDGs: Understanding and Improving the Role of Corporate Transparency through Big Data

July 16, 2018, São Paulo, SP, Brazil

Transformative Forces

C

A

M

S

+ Information Security

Trends in the use of "technology in business" (or business in technology) for the next decade

loud

nalytics

obile

ocial Business

Analytical Trends

Source: Gartner Group, IDC, IBM CAMS 2014

Source: OECD Science, Technology and Innovation Outlook 2016




• Big Data is just another “publicity stunt” .

• Propelled by technology companies and systems consulting

companies, the term Big Data remains poorly defined .

• This is intentional and allows vendors to spread what they want

because their markets do not know exactly what Big Data means ...

• In general, technology companies use the term Big Data to refer to

large volumes of data from new sources .

• This fact, however, is nothing new. Since the advent of computers,

every year more data is accumulated and new sources emerge ...

BIG DATA

A “publicity stunt”?

Source: Stephen Few – Visual Business Intelligence, October 31, 2013

“

”




is like

TEENAGE SEX

Everyone talks about it,

Nobody really knows how to do it,

Everyone thinks everyone else is doing it,

So everyone claims they are doing it.

BIG DATA

Dan Ariely

BIG DATA




“Big Data” are data and processes whose scale, distribution, diversity and / or creation speed requires the

use of new storage and analysis technologies to allow the capture of the value inserted in them(Adapted from EMC, 2013)

1. Data VolumeBillions of lines x billions of columns

Increase of 44 times from 2009 to 2020 (0,9ZB to 35ZB)

2. Processing ComplexityData structures are constantly changing

Need to analyze such data in real time

3. Data StructureLarge variety (80-90% unstructured) to be analyzed

These characteristics make it necessary to use parallel andparallel mass computing (MPP) systems

BIG DATA – “Definition”

Source: EMC (2013)




It is not so new...




Velocity

Volume

Variety

MB GB TB PB

audio

Expanding at an increasing rate

on three fronts (the first 3 Vs)

3 Vs




Velocity

Volume

Variety

MB GB TB PB

audio



+ Veracity

+ Value

5 Vs




Velocity

Volume

Variety

MB GB TB PB

audio



+ Veracity

+ Value

+ Variability

+ Visualization

7 Vs




An Ambitious Profile

Data Scientist

Source: CARVALHO, 2017




BIG DATA

2000’s1990’s 2010’s

INF

OR

MA

TIO

N V

OL

UM

E

LARGE

SMALL

MEASURED BY

TERABYTES1TB = 1,000GB

MEASURED BY

PETABYTES1PB = 1,000TB

MEASURED BY

EXABYTES1EB = 1,000PB

StructuredData

Familiar.

Ex.: Relational DBs

Semi-Structured Data

Ex.: XML Data,

JSON Data

Quasi-Structured Data

Ex.: Clickstream

Unstructured

Data

Ex.: Text, Photo, Video

General sensors

Big Variety

Source: EMC (2013),MIRAPALHETA (2015)




BIG DATA

2000’s1990’s 2010’s

INF

OR

MA

TIO

N V

OL

UM

E

LARGE

SMALL

MEASURED BY

TERABYTES1TB = 1,000GB

MEASURED BY

PETABYTES1PB = 1,000TB

MEASURED BY

EXABYTES1EB = 1,000PB

Source: EMC (2013),MIRAPALHETA (2015)

Data Islands Data Warehouses Analytic Sandbox

New Technologies andNew Organization!!




Laboratory

RStudio

Setting, Ambiance and Tools

http://madlib.net/index.html

http://madlib.net/index.html

http://www.google.com.br/imgres?imgurl=http://geoadvantage.com/images/training/arc_view_logo.gif&imgrefurl=http://www.geoadvantage.com/training/intro_arcview.html&usg=__IcAFX4di73Hn_2dqpTfo9ZJCYFw=&h=84&w=100&sz=3&hl=pt-BR&start=20&zoom=1&itbs=1&tbnid=hPBAn_kJ0V3tOM:&tbnh=69&tbnw=82&prev=/search?q=arcview+logotipo&hl=pt-BR&biw=1260&bih=620&gbv=2&tbm=isch&ei=FbgPTvDgCsG9tgexksT9DQ

http://www.google.com.br/imgres?imgurl=http://geoadvantage.com/images/training/arc_view_logo.gif&imgrefurl=http://www.geoadvantage.com/training/intro_arcview.html&usg=__IcAFX4di73Hn_2dqpTfo9ZJCYFw=&h=84&w=100&sz=3&hl=pt-BR&start=20&zoom=1&itbs=1&tbnid=hPBAn_kJ0V3tOM:&tbnh=69&tbnw=82&prev=/search?q=arcview+logotipo&hl=pt-BR&biw=1260&bih=620&gbv=2&tbm=isch&ei=FbgPTvDgCsG9tgexksT9DQ

http://www.google.com.br/imgres?imgurl=http://geoplan.asu.edu/enews-1209/images/geoda_logo.jpg&imgrefurl=http://geoplan.asu.edu/enews-1209/geodacenter.htm&usg=___D9TXJJO8JGqpw82yUaPaR3dp2c=&h=128&w=169&sz=39&hl=pt-BR&start=1&zoom=1&itbs=1&tbnid=ZtWIHFcJg0k7iM:&tbnh=75&tbnw=99&prev=/search?q=geoda+logotipo&hl=pt-BR&biw=1260&bih=620&gbv=2&tbm=isch&ei=NLgPTsCMDsuhtwfmuej4DQ

http://www.google.com.br/imgres?imgurl=http://geoplan.asu.edu/enews-1209/images/geoda_logo.jpg&imgrefurl=http://geoplan.asu.edu/enews-1209/geodacenter.htm&usg=___D9TXJJO8JGqpw82yUaPaR3dp2c=&h=128&w=169&sz=39&hl=pt-BR&start=1&zoom=1&itbs=1&tbnid=ZtWIHFcJg0k7iM:&tbnh=75&tbnw=99&prev=/search?q=geoda+logotipo&hl=pt-BR&biw=1260&bih=620&gbv=2&tbm=isch&ei=NLgPTsCMDsuhtwfmuej4DQ




Business Intelligence / KPIs / Dashboards

Basket Analysis

Scenaries and Linear Predictions

Scoring / Trees / Logistic Regression

Clusters Analysis / Life Cycle Analysis

Otimization / System Dynamics

Time Series / Survival Analysis

Machine Learning / Deep Learning

Model Combinations

Geoanalisys / Network Analysis / Text Mining

Complexity of the analysis

Numberof users

Exploratory Data Analysis

“Watershed”

AnalyticalInnovators

Analytical Techniques

Source: KUGLER, 2017




An expansion / revision in positioning

Source: OLLION, 2018

Big Data depends on the availability and a multiplicity of sources





Source: LETOUZÉ, 2017; KING, 2013

Big Data is Not About the Data!! (KING, 2013)





Source: LETOUZÉ, 2017




Challenges in the Big Data Era

• Big Data, Data Science, Analytics ...• Challenge in the integration of analytical

techniques• AI, Neuroscience applied to marketing and business,

spatial statistics, behavioral models• Models to handle stakeholders’ relationships

• Computational Challenge• Challenge in the adoption of AI and Data

Science by companies and public organizations• Adoption of forceps, apart from core management• Analytical Sandbox vs. IT Policy

• Cultural challenge - new skills of analytical teams and managers




• ESG – Big Data perspective• Identification/Generation of Environmental, Social

and Corporate Governance Factors through out automated collection methods (API, Web Scrapping, Interpretation of Public Images, etc)

• Establish a plan for obtaining indicators through the Big Data perspective and compare them with indicators traditionally collected

• Study the effect of sustainability in the company image

• Cultural challenge - new skills of analytical teams and managers

Challenges... for SDG’s and ESG




SOCIAL

Issues related to the rights, well-being and interests of individuals and communities.They include:• Human rights• Labor rights in the value chain• Child, slave or degrading work• Health and safety at Work• Freedom of association and expression• Human capital management and labor relations• Diversity• Relationship with local communities• Activities in conflict regions• Health and access to medicine• HIV / AIDS• Protection of consumer rights• Controversial weapons


ENVIRONMENTAL

Issues relating to the quality of thefunctioning of the environment andecosystems. They include:• Loss of biodiversity• Greenhouse gas emissions, GHG Protocol• Climate changes• Renewable energy• Energy efficiency• Shortage or pollution of water, airand other resources• Changes in land use• Acidification of the oceans• Nitrogen and phosphorus cycles

Source: UNPRI, 2016; NAUM, 2018

Model of ESG Factors

GOVERNANCE

Issues related to the governance of investees. In the context of listed companies, they include:• Structure, size, diversity, competencies and independence of the Board of Directors;• Remuneration of executives;• Shareholder rights and interactions;• Disclosure of information to the market;• Business ethics;• Bribery and corruption;• Internal controls and risk management;• Relationship between Board, executives, shareholders and stakeholders;• Strategic positioning, covering impacts of ASG issues and implementation of strategies.In the case of unlisted companies, issues also include matters relating to the governance of the fund, such as the powers of the Committees, valuation issues, fee structures, etc.

Potential to be collected via “Big Data” methods




• SDG’s• Discussion of how Big Data can help monitor

and manage SDG’s integrating traditional data with more granular & disaggregated data• E.g., alternative indicators with utilities/telco data

• Establish a plan for obtaining indicators through the Big Data perspective and compare them with indicators traditionally collected

• Cultural challenge - new skills of analytical teams, NOG and public managers (“C”ommunities layer)






• Alternative Methods in Quantitative Research for traditionally

complex Census Surveys

Mapeamento de População a partir de Registro de Chamadas Telefônicas

Source: http://mundogeo.com/blog/2014/11/04/pesquisadores-realizam-mapeamento-de-populacao-com-dados-de-registro-de-chamadas/





Source: Bloomberg Spacknow: https://spaceknow.com/ apud MARSITCH, 2017

• Object Pattern Recognition - Aerial Images


https://spaceknow.com/




Source: MARSITCH, 2017

• Recognition of Patterns of Objects allied to the Granting of Credit

• E.g., probability of being a pharmaceutical company with an

annual turnover of R$ 100,000, with a geocoded and certified

address





Microcredit Score and Socio-Economic Indicators based on Electric Energy


Source: FRANCISCO, 2010




• Development of an Indicator of Propensity to Energy Commercial Losses using Geospatial Statistical Techniques and Socio-Economic Data: the Case of AES EletropauloFRANCISCO, E., FAGUNDES, E., PONCHIO, M., ZAMBALDI, F. - EnANPAD 2009

• Use of geospatial techniques for the design and operation of a fraud-prone indicator, and analysis (through GWR) of the association between this indicator and customer satisfaction

Análises Preditivas

Source: FRANCISCO et al., 2010




Algoritmos eModelos

Preditivos

sons videosimagens

dados

`layers (mapas)

informações

Spatial Data Warehouse

Usuáriodecisor

dadosinformações

Plataforma Analítica

(desenho de cenários, representações cognitivas)

estudossob demanda

dados

dados

dados

dados

videos

dados

notícias

dados

dados

dados

dados

dados

sons

dados

dadosdados

dados

dados

dados

dadosinformações

imagens dados

apoioconsultivo

Aquisição decultura e

know-how

i

i

i

tren

ds

autenticaçãoconectores

GESTOR

Framework: Adaptive Business Intelligence and Big Data

Source: LETOUZÉ, 2017;MICHALEWICZ et al., 2006 ;GisBI, 2015




References

CARVALHO, A. Analytics como uma Ferramenta para Consumer Insights. Master Dissertation. ESPM - Professional Master in Consumer Behaviour. 2017.

EMC. Data Science & Analytics Course, 2013.

FRANCISCO, E. R.; FAGUNDES, E. B. ; PONCHIO, Mateus ; ZAMBALDI, F. . Development of an Indicator of Propensity to Energy Commercial Losses Using Geospatial Statistical

Techniques and Socio-Economic Data: The Case of AES Eletropaulo. RAM. Revista de Administração Mackenzie (Online), v. 11, p. 178-197, 2010.

FRANCISCO, Eduardo. Indicadores de Renda baseados em Consumo de Energia Elétrica: Abordagens Domiciliar e Regional na Perspectiva da Estatística Espacial. Tese (Doutorado em

Administração) – Escola de Administração de Empresas de São Paulo, Fundação Getulio Vargas, São Paulo, 2010.

GisBI. Systemic Geospatial Big Data. 2015.

KING, G. Big Data is not About the Data! Talk at New England AI Meetup, 2013.

KUGLER, J. L. COMPETÊNCIA ANALÍTICA: Conceitos e Estratégias para a Construção da Empresa Inteligente. São Paulo, SP: Editora Saraiva, 2013. 288 p.

KUGLER, J. L. Fundamentals of Big Data. Class Notes. FGV-EAESP, 2017

LETOUZÉ, E. Monitoring and Promoting the SDGs Through the (Big) Data Revolution. Big Data for Measuring Digital Economy - Professional Training Workshop. CETIC/NIC.br .São Paulo,

Brazil, 2017.

MARSITCH, M. A. P. GeoInteligência aplicada a Crédito e Gestão Financeira. MBA de Big Data Analytics, FGV Management. 2018.

MICHALEWICZ, Z., SCHMIDT, M., MICHALEWICZ, M., CHIRIAC, C. Adaptive Business Intelligence, Springer, 2016.

MIRAPALHETA, G. Curso de Data Science & Big Data Analytics, 2015.

NAUM, C. Indicadores Intangíveis: Motivações para adoção dos fatores ASG às análises de investimento pelos gestores de ativos. Master Dissertation. ESPM - Professional Master in

Consumer Behaviour. 2018.

OECD Science, Technology and Innovation Outlook 2016 - Available in http://www.oecd.org/sti/oecd-science-technology-and-innovation-outlook-25186167.htm

OLLION, E. Beyond big data. States, companies and scientists face the digital revolution. CNRS, 2018.

UNPRI – Principles Responsible Investments. Available in: www.unpri.org/. 2016.

Invitation

Thank you!Obrigado!


Advancing the SDGs: Understanding and Improving the Role of Corporate Transparency through Big Data, São Paulo, SP, Brazil, July 16-17, 2018

SDG’s and ESG · Source: OECD Science, Technology and Innovation Outlook 2016. Eduardo de Rezende...

Documents

Transcript of SDG’s and ESG · Source: OECD Science, Technology and Innovation Outlook 2016. Eduardo de Rezende...